【建模案例】通过日志查询每日时点状态
- 这条 SQL 难度不低，能轻松写出来，足以证明你的技术功底扎实；但若是日常业务里非要写这类复杂 SQL，往往意味着数仓建模环节出了问题。
- 本文同步发表在数据仓库技术网站dwsql.com 的数仓建模->数据仓库建模案例 下
- 如果该需求作为面试题目，要求使用非笛卡尔积的方式写SQL，难度应该数据困难，要高过一般的连续问题，所以不要轻易拿去考别人
背景
这个内容是群友问我的一个问题，他已经解决了，但是通过表关联，使用笛卡尔积的方式进行处理，为了进一步优化，他问我有没有不用笛卡尔积的方式计算。
1.需求内容
有用户盈利记录表 t_case1_ods_user_profit,包含三个字段，分别为交易日期，用户ID，用户当日盈利金额（如果亏损则为负数）；样例数据如下：
+-------------+----------+---------+
| trade_date | user_id | profit |
+-------------+----------+---------+
| 2026-03-12 | 1001 | 3.00 |
| 2026-03-13 | 1002 | 5.00 |
| 2026-03-13 | 1001 | -5.00 |
| 2026-03-14 | 1003 | 7.00 |
| 2026-03-14 | 1004 | 7.00 |
| 2026-03-14 | 1002 | 7.00 |
| 2026-03-15 | 1002 | -7.00 |
| 2026-03-15 | 1001 | -7.00 |
+-------------+----------+---------+
建表语句
create table t_case1_ods_user_profit(
trade_date string COMMENT '交易日期 yyyy-mm-dd',
user_id bigint comment '用户ID',
profit decimal(10,2) COMMENT '盈利金额'
) COMMENT='用户每日盈亏表';
INSERT INTO TABLE t_case1_ods_user_profit VALUES
('2026-03-12', 1001, 3),
('2026-03-13', 1002, 5),
('2026-03-13', 1001, -5),
('2026-03-14', 1003, 7),
('2026-03-14', 1004, 7),
('2026-03-14', 1002, 7),
('2026-03-15', 1002, -7),
('2026-03-15', 1001, -7);
需求内容: 希望能够查询下面数据
用户盈利记录表中，记录了每日有交易过程的用户当期盈利金额，如果亏损则记为负数。现希望查询出截止到每日的累积盈利的用户数；
分析： 因为盈利记录表中仅存在当日有交易记录，这样我们进行累积求和的结果是不能满足要求的，这是该题目困难的原因。
SQL 解法
我们先使用SQL完成需求内容。
方法一：
既然题目中没有，那么我们将其补全，即为每个用户补全所有日期的交易记录，如果用户不存在交易记录，则补当日盈亏金额为0；
- 1.先做笛卡尔积，算出一个全量用户全日期，为0的交易记录
select t_date.trade_date,
t_user.user_id,
0 as profit
from (select trade_date
from t_case1_ods_user_profit
group by trade_date) t_date
join (select user_id
from t_case1_ods_user_profit
group by user_id) t_user;
执行结果
+-------------+----------+---------+
| trade_date | user_id | profit |
+-------------+----------+---------+
| 2026-03-12 | 1002 | 0 |
| 2026-03-12 | 1001 | 0 |
| 2026-03-12 | 1003 | 0 |
| 2026-03-12 | 1004 | 0 |
| 2026-03-14 | 1002 | 0 |
| 2026-03-14 | 1001 | 0 |
| 2026-03-14 | 1003 | 0 |
| 2026-03-14 | 1004 | 0 |
| 2026-03-13 | 1002 | 0 |
| 2026-03-13 | 1001 | 0 |
| 2026-03-13 | 1003 | 0 |
| 2026-03-13 | 1004 | 0 |
| 2026-03-15 | 1002 | 0 |
| 2026-03-15 | 1001 | 0 |
| 2026-03-15 | 1003 | 0 |
| 2026-03-15 | 1004 | 0 |
+-------------+----------+---------+
16 rows selected (10.268 seconds)(dwsql.com)
- 2.全量交易记录与原始交易记录进行合并，如果用户有交易记录就取实际交易记录，否则使用补充的当日盈亏为0的交易记录；
select trade_date,
user_id,
sum(profit) as profit
from (select t_date.trade_date,
t_user.user_id,
0 as profit
from (select trade_date
from t_case1_ods_user_profit
group by trade_date) t_date
join (select user_id
from t_case1_ods_user_profit
group by user_id) t_user
union all
select trade_date, user_id, profit
from t_case1_ods_user_profit) t
group by trade_date,
user_id
order by trade_date,
user_id --为方便查看数据添加
执行结果
+-------------+----------+---------+
| trade_date | user_id | profit |
+-------------+----------+---------+
| 2026-03-12 | 1001 | 3.00 |
| 2026-03-12 | 1002 | 0.00 |
| 2026-03-12 | 1003 | 0.00 |
| 2026-03-12 | 1004 | 0.00 |
| 2026-03-13 | 1001 | -5.00 |
| 2026-03-13 | 1002 | 5.00 |
| 2026-03-13 | 1003 | 0.00 |
| 2026-03-13 | 1004 | 0.00 |
| 2026-03-14 | 1001 | 0.00 |
| 2026-03-14 | 1002 | 7.00 |
| 2026-03-14 | 1003 | 7.00 |
| 2026-03-14 | 1004 | 7.00 |
| 2026-03-15 | 1001 | -7.00 |
| 2026-03-15 | 1002 | -7.00 |
| 2026-03-15 | 1003 | 0.00 |
| 2026-03-15 | 1004 | 0.00 |
+-------------+----------+---------+
16 rows selected (1.249 seconds)(dwsql.com)
- 3.累积求和得到每个用户截止到每天的的累积盈利金额；
select trade_date,
user_id,
profit,
sum(profit) over (partition by user_id order by trade_date asc) as total_profit --关键开窗函数
from (select trade_date,
user_id,
sum(profit) as profit
from (select t_date.trade_date,
t_user.user_id,
0 as profit
from (select trade_date
from t_case1_ods_user_profit
group by trade_date) t_date
join (select user_id
from t_case1_ods_user_profit
group by user_id) t_user
union all
select trade_date, user_id, profit
from t_case1_ods_user_profit) t
group by trade_date,
user_id) tt
执行结果
+-------------+----------+---------+---------------+
| trade_date | user_id | profit | total_profit |
+-------------+----------+---------+---------------+
| 2026-03-12 | 1001 | 3.00 | 3.00 |
| 2026-03-13 | 1001 | -5.00 | -2.00 |
| 2026-03-14 | 1001 | 0.00 | -2.00 |
| 2026-03-15 | 1001 | -7.00 | -9.00 |
| 2026-03-12 | 1002 | 0.00 | 0.00 |
| 2026-03-13 | 1002 | 5.00 | 5.00 |
| 2026-03-14 | 1002 | 7.00 | 12.00 |
| 2026-03-15 | 1002 | -7.00 | 5.00 |
| 2026-03-12 | 1003 | 0.00 | 0.00 |
| 2026-03-13 | 1003 | 0.00 | 0.00 |
| 2026-03-14 | 1003 | 7.00 | 7.00 |
| 2026-03-15 | 1003 | 0.00 | 7.00 |
| 2026-03-12 | 1004 | 0.00 | 0.00 |
| 2026-03-13 | 1004 | 0.00 | 0.00 |
| 2026-03-14 | 1004 | 7.00 | 7.00 |
| 2026-03-15 | 1004 | 0.00 | 7.00 |
+-------------+----------+---------+---------------+
16 rows selected (0.952 seconds)(dwsql.com)
- 4.统计每天累积盈利金额>0的用户数；
select trade_date, count(case when total_profit > 0 then user_id end) as user_cnt
from (select trade_date,
user_id,
profit,
sum(profit) over (partition by user_id order by trade_date asc) as total_profit
from (select trade_date,
user_id,
sum(profit) as profit
from (select t_date.trade_date,
t_user.user_id,
0 as profit
from (select trade_date
from t_case1_ods_user_profit
group by trade_date) t_date
join (select user_id
from t_case1_ods_user_profit
group by user_id) t_user
union all
select trade_date, user_id, profit
from t_case1_ods_user_profit) t
group by trade_date,
user_id) tt) ttt
group by trade_date
order by trade_date asc;
执行结果
+-------------+-----------+
| trade_date | user_cnt |
+-------------+-----------+
| 2026-03-12 | 1 |
| 2026-03-13 | 1 |
| 2026-03-14 | 3 |
| 2026-03-15 | 3 |
+-------------+-----------+
4 rows selected (1.252 seconds)(dwsql.com)
方法二：
前面先做笛卡尔积，太过“暴力” ，会产生大量的数据，产生大量计算；我们稍微优化一下；
- 1.先对所有用户进行累积求和；
select trade_date,
user_id,
profit,
sum(profit) over (partition by user_id order by trade_date asc) as total_profit --累积求和
from t_case1_ods_user_profit
执行结果
+-------------+----------+---------+---------------+
| trade_date | user_id | profit | total_profit |
+-------------+----------+---------+---------------+
| 2026-03-12 | 1001 | 3.00 | 3.00 |
| 2026-03-13 | 1001 | -5.00 | -2.00 |
| 2026-03-15 | 1001 | -7.00 | -9.00 |
| 2026-03-13 | 1002 | 5.00 | 5.00 |
| 2026-03-14 | 1002 | 7.00 | 12.00 |
| 2026-03-15 | 1002 | -7.00 | 5.00 |
| 2026-03-14 | 1003 | 7.00 | 7.00 |
| 2026-03-14 | 1004 | 7.00 | 7.00 |
+-------------+----------+---------+---------------+
8 rows selected (0.352 seconds)(dwsql.com)
**说明：**我们没有办法对该数据进行直接统计，因为如果用户在某天不存在交易，则当日不会有其记录，e.g. 1001 用户在3月14日没有记录，所以接下来我们要使用笛卡尔积来完成缺失数据的补足；
- 2.通过查询记录表，查到所有的日期，日期与累积求和结果进行笛卡尔积计算，限定条件为累积求和日期 <= 维表日期，并根据日期和用户ID进行分组，按照日期倒排进行排序开窗，得到行号
select t_date.trade_date,
t_profit.trade_date,
t_profit.user_id,
t_profit.profit,
t_profit.total_profit,
row_number() over (partition by t_date.trade_date, t_profit.user_id order by t_profit.trade_date desc) as rn --注意倒排序
from (select trade_date
from t_case1_ods_user_profit
group by trade_date) t_date
join (select trade_date,
user_id,
profit,
sum(profit) over (partition by user_id order by trade_date asc) as total_profit
from t_case1_ods_user_profit) t_profit
--注意没有on条件，所以是笛卡尔积
where t_profit.trade_date <= t_date.trade_date
执行结果
+-------------+-------------+----------+---------+---------------+-----+
| trade_date | trade_date | user_id | profit | total_profit | rn |
+-------------+-------------+----------+---------+---------------+-----+
| 2026-03-12 | 2026-03-12 | 1001 | 3.00 | 3.00 | 1 |
| 2026-03-13 | 2026-03-13 | 1001 | -5.00 | -2.00 | 1 |
| 2026-03-13 | 2026-03-12 | 1001 | 3.00 | 3.00 | 2 |
| 2026-03-13 | 2026-03-13 | 1002 | 5.00 | 5.00 | 1 |
| 2026-03-14 | 2026-03-13 | 1001 | -5.00 | -2.00 | 1 |
| 2026-03-14 | 2026-03-12 | 1001 | 3.00 | 3.00 | 2 |
| 2026-03-14 | 2026-03-14 | 1002 | 7.00 | 12.00 | 1 |
| 2026-03-14 | 2026-03-13 | 1002 | 5.00 | 5.00 | 2 |
| 2026-03-14 | 2026-03-14 | 1003 | 7.00 | 7.00 | 1 |
| 2026-03-14 | 2026-03-14 | 1004 | 7.00 | 7.00 | 1 |
| 2026-03-15 | 2026-03-15 | 1001 | -7.00 | -9.00 | 1 |
| 2026-03-15 | 2026-03-13 | 1001 | -5.00 | -2.00 | 2 |
| 2026-03-15 | 2026-03-12 | 1001 | 3.00 | 3.00 | 3 |
| 2026-03-15 | 2026-03-15 | 1002 | -7.00 | 5.00 | 1 |
| 2026-03-15 | 2026-03-14 | 1002 | 7.00 | 12.00 | 2 |
| 2026-03-15 | 2026-03-13 | 1002 | 5.00 | 5.00 | 3 |
| 2026-03-15 | 2026-03-14 | 1003 | 7.00 | 7.00 | 1 |
| 2026-03-15 | 2026-03-14 | 1004 | 7.00 | 7.00 | 1 |
+-------------+-------------+----------+---------+---------------+-----+
18 rows selected (0.809 seconds)
- 3.取行号= 1 的记录，统计累积盈利金额>0 的用户数据
select new_trade_date,
count(case when total_profit > 0 then user_id end) as user_cnt
from (select t_date.trade_date as new_trade_date,
t_profit.trade_date,
t_profit.user_id,
t_profit.profit,
t_profit.total_profit,
row_number() over (partition by t_date.trade_date, t_profit.user_id order by t_profit.trade_date desc) as rn --注意倒排序
from (select trade_date
from t_case1_ods_user_profit
group by trade_date) t_date
join (select trade_date,
user_id,
profit,
sum(profit) over (partition by user_id order by trade_date asc) as total_profit
from t_case1_ods_user_profit) t_profit
--注意没有on条件，所以是笛卡尔积
where t_profit.trade_date <= t_date.trade_date) t
where t.rn = 1
group by new_trade_date
order by new_trade_date;
执行结果
+-----------------+-----------+
| new_trade_date | user_cnt |
+-----------------+-----------+
| 2026-03-12 | 1 |
| 2026-03-13 | 1 |
| 2026-03-14 | 3 |
| 2026-03-15 | 3 |
+-----------------+-----------+
4 rows selected (11.858 seconds)
方法三：
方法二还是笛卡尔积，数据还是很大，我们换一种思路来计算，避免笛卡尔积。因为只是想要盈利用户数量，并不需要找出具体是哪个用户，所以可以使用累加的方式。
- 1.先对所有用户进行累积求和；
select trade_date,
user_id,
profit,
sum(profit) over (partition by user_id order by trade_date asc) as total_profit --累积求和
from t_case1_ods_user_profit
执行结果
+-------------+----------+---------+---------------+
| trade_date | user_id | profit | total_profit |
+-------------+----------+---------+---------------+
| 2026-03-12 | 1001 | 3.00 | 3.00 |
| 2026-03-13 | 1001 | -5.00 | -2.00 |
| 2026-03-15 | 1001 | -7.00 | -9.00 |
| 2026-03-13 | 1002 | 5.00 | 5.00 |
| 2026-03-14 | 1002 | 7.00 | 12.00 |
| 2026-03-15 | 1002 | -7.00 | 5.00 |
| 2026-03-14 | 1003 | 7.00 | 7.00 |
| 2026-03-14 | 1004 | 7.00 | 7.00 |
+-------------+----------+---------+---------------+
8 rows selected (0.352 seconds)(dwsql.com)
- 2.使用lag函数获得每个用户前一行数据的累计盈利金额
select trade_date,
user_id,
profit,
total_profit,
lag(total_profit) over (partition by user_id order by trade_date) as last_total_profit
from (select trade_date,
user_id,
profit,
sum(profit) over (partition by user_id order by trade_date asc) as total_profit --累积求和
from t_case1_ods_user_profit) t
执行结果
+-------------+----------+---------+---------------+--------------------+
| trade_date | user_id | profit | total_profit | last_total_profit |
+-------------+----------+---------+---------------+--------------------+
| 2026-03-12 | 1001 | 3.00 | 3.00 | NULL |
| 2026-03-13 | 1001 | -5.00 | -2.00 | 3.00 |
| 2026-03-15 | 1001 | -7.00 | -9.00 | -2.00 |
| 2026-03-13 | 1002 | 5.00 | 5.00 | NULL |
| 2026-03-14 | 1002 | 7.00 | 12.00 | 5.00 |
| 2026-03-15 | 1002 | -7.00 | 5.00 | 12.00 |
| 2026-03-14 | 1003 | 7.00 | 7.00 | NULL |
| 2026-03-14 | 1004 | 7.00 | 7.00 | NULL |
+-------------+----------+---------+---------------+--------------------+
8 rows selected (8.925 seconds)
- 3.对比total_profit 和 last_total_profit，根据两者关系做如下判断，添加人数变化列 cnt_change
- last_total_profit 为空，total_profit >0,则 cnt_change =1
- last_total_profit 为空，total_profit <0,则 cnt_change = 0
- last_total_profit >0,total_profit >0,则 cnt_change =0
- last_total_profit <=0,total_profit <=0, 则 cnt_change =0
- last_total_profit >0,total_profit <=0 则 cnt_change = - 1
- last_total_profit <=0,total_profit >0,则 cnt_change = 1
select trade_date,
user_id,
profit,
total_profit,
last_total_profit,
case
when last_total_profit is null and total_profit > 0 then 1
when last_total_profit is null and total_profit < 0 then 0
when last_total_profit > 0 and total_profit > 0 then 0
when last_total_profit <= 0 and total_profit <= 0 then 0
when last_total_profit > 0 and total_profit <= 0 then -1
when last_total_profit <= 0 and total_profit > 0 then 1
end as cnt_change
from (select trade_date,
user_id,
profit,
total_profit,
lag(total_profit) over (partition by user_id order by trade_date) as last_total_profit
from (select trade_date,
user_id,
profit,
sum(profit) over (partition by user_id order by trade_date asc) as total_profit --累积求和
from t_case1_ods_user_profit) t) tt
执行结果
+-------------+----------+---------+---------------+--------------------+-------------+
| trade_date | user_id | profit | total_profit | last_total_profit | cnt_change |
+-------------+----------+---------+---------------+--------------------+-------------+
| 2026-03-12 | 1001 | 3.00 | 3.00 | NULL | 1 |
| 2026-03-13 | 1001 | -5.00 | -2.00 | 3.00 | -1 |
| 2026-03-15 | 1001 | -7.00 | -9.00 | -2.00 | 0 |
| 2026-03-13 | 1002 | 5.00 | 5.00 | NULL | 1 |
| 2026-03-14 | 1002 | 7.00 | 12.00 | 5.00 | 0 |
| 2026-03-15 | 1002 | -7.00 | 5.00 | 12.00 | 0 |
| 2026-03-14 | 1003 | 7.00 | 7.00 | NULL | 1 |
| 2026-03-14 | 1004 | 7.00 | 7.00 | NULL | 1 |
+-------------+----------+---------+---------------+--------------------+-------------+
8 rows selected (0.52 seconds)(dwsql.com)
- 4.每日人数变化求和后再对结果累计求和得到结果
select trade_date,
sum(day_cnt_change) over (order by trade_date) as user_cnt
from (select trade_date,
sum(cnt_change) as day_cnt_change
from (select trade_date,
user_id,
profit,
total_profit,
last_total_profit,
case
when last_total_profit is null and total_profit > 0 then 1
when last_total_profit is null and total_profit < 0 then 0
when last_total_profit > 0 and total_profit > 0 then 0
when last_total_profit <= 0 and total_profit <= 0 then 0
when last_total_profit > 0 and total_profit <= 0 then -1
when last_total_profit <= 0 and total_profit > 0 then 1
end as cnt_change
from (select trade_date,
user_id,
profit,
total_profit,
lag(total_profit) over (partition by user_id order by trade_date) as last_total_profit
from (select trade_date,
user_id,
profit,
sum(profit) over (partition by user_id order by trade_date asc) as total_profit --累积求和
from t_case1_ods_user_profit) t) tt) ttt
group by trade_date) tttt
执行结果
+-------------+-----------+
| trade_date | user_cnt |
+-------------+-----------+
| 2026-03-12 | 1 |
| 2026-03-13 | 1 |
| 2026-03-14 | 3 |
| 2026-03-15 | 3 |
+-------------+-----------+
4 rows selected (1.883 seconds)
分析
SQL我们写完了，即便按照方法三，没有笛卡尔积的方式，每次查询我们都需要不断的查询所有历史数据，进行一遍遍的开窗和聚合，这在生产过程是不可接受的。所以如果日常需要查看该数据、或者经常性的分析，则需要通过建模的方式减少类似的查询计算。
解决思路
如果我们一张表记录了截止到前天所有用户的用户累计盈利金额，叠加昨天用户盈利金额，就可以算出截止到昨天的用户累计盈利金额了。 这种方式叫做滚表的方式，这样加工时每天仅需要处理当天的日期和全量用户前一天的状态，查询是直接根据日期进行统计即可拿到统计结果。 这也是通过存储换计算的方式，也可以理解为空间换时间（存储空间换查询时的查询时间）
创建表
为了方便加工，我们创建两张表 用户每日盈亏表（分区表）（注：该表在实际生产环境，用户每日盈亏表应该就已经是分区表了，无需我们额外处理），用户每日累积盈利金额状态表（分区表）。
CREATE TABLE t_case1_dwd_user_profit (
user_id BIGINT COMMENT '用户ID',
profit DECIMAL(10,2) COMMENT '盈利金额'
)
COMMENT '用户每日盈亏表'
PARTITIONED BY (trade_date STRING COMMENT '交易日期 yyyy-mm-dd');
CREATE TABLE IF NOT EXISTS t_case1_dwd_user_profit_status (
user_id BIGINT COMMENT '用户ID',
profit DECIMAL(10,2) COMMENT '当日盈利金额',
total_profit DECIMAL(18,2) COMMENT '累计盈利金额'
)
COMMENT '用户盈利状态明细表'
PARTITIONED BY (stat_date STRING COMMENT '统计日期 yyyy-mm-dd');
生产数据恢复
默认原始日志数据是分区表内数据，我们将数据恢复到分区表中，方便进行下一步处理。（当然对于写SQL来讲，是否做分区无所谓，但是在工作中，一定会有）
insert overwrite table t_case1_dwd_user_profit partition(trade_date)
select
user_id,
profit,
trade_date
from t_case1_ods_user_profit;
建模
滚表的初始数据、初始加工过程和后面的加工过程不相同，所以需要写两个逻辑，初始逻辑和滚动逻辑。
- 1.初始数据 因为最早日期是3月12日，把3月12日作为初始数据。
select
insert overwrite table t_case1_dwd_user_profit_status partition (stat_date)
select user_id,
profit,
profit as total_profit
trade_date as stat_date
from t_case1_dwd_user_profit
where trade_date = '2026-03-12'
- 2.滚表加工(核心步骤) 13日数据加工
insert overwrite table t_case1_dwd_user_profit_status partition (stat_date = '2026-03-13')
select coalesce(t1.user_id, t2.user_id) as user_id,
t2.profit,
coalesce(t1.total_profit,0) + coalesce(t2.profit, 0) as total_profit
from (
--昨日状态数据
select user_id,
profit,
total_profit,
stat_date
from t_case1_dwd_user_profit_status
where stat_date = '2026-03-12') t1
full outer join
(select user_id,
profit,
trade_date as stat_date
from t_case1_dwd_user_profit
where trade_date = '2026-03-13'
) t2
on t1.user_id = t2.user_id
14日加工
insert overwrite table t_case1_dwd_user_profit_status partition (stat_date = '2026-03-14')
select coalesce(t1.user_id, t2.user_id) as user_id,
t2.profit,
coalesce(t1.total_profit,0) + coalesce(t2.profit, 0) as total_profit
from (
--昨日状态数据
select user_id,
profit,
total_profit,
stat_date
from t_case1_dwd_user_profit_status
where stat_date = '2026-03-13') t1
full outer join
(select user_id,
profit,
trade_date as stat_date
from t_case1_dwd_user_profit
where trade_date = '2026-03-14'
) t2
on t1.user_id = t2.user_id
15日加工
insert overwrite table t_case1_dwd_user_profit_status partition (stat_date = '2026-03-15')
select coalesce(t1.user_id, t2.user_id) as user_id,
t2.profit,
coalesce(t1.total_profit,0) + coalesce(t2.profit, 0) as total_profit
from (
--昨日状态数据
select user_id,
profit,
total_profit,
stat_date
from t_case1_dwd_user_profit_status
where stat_date = '2026-03-14') t1
full outer join
(select user_id,
profit,
trade_date as stat_date
from t_case1_dwd_user_profit
where trade_date = '2026-03-15'
) t2
on t1.user_id = t2.user_id
后续日期根据实际情况加工，生产环境使用参数每日处理即可，最终分区表内的数据如下
结果数据
+----------+---------+---------------+-------------+
| user_id | profit | total_profit | stat_date |
+----------+---------+---------------+-------------+
| 1001 | 3.00 | 3.00 | 2026-03-12 |
| 1001 | -5.00 | -2.00 | 2026-03-13 |
| 1002 | 5.00 | 5.00 | 2026-03-13 |
| 1001 | NULL | -2.00 | 2026-03-14 |
| 1002 | 7.00 | 12.00 | 2026-03-14 |
| 1003 | 7.00 | 7.00 | 2026-03-14 |
| 1004 | 7.00 | 7.00 | 2026-03-14 |
| 1001 | -7.00 | -9.00 | 2026-03-15 |
| 1002 | -7.00 | 5.00 | 2026-03-15 |
| 1003 | NULL | 7.00 | 2026-03-15 |
| 1004 | NULL | 7.00 | 2026-03-15 |
+----------+---------+---------------+-------------+
11 rows selected (0.525 seconds)
- 3.查询使用
select stat_date,
count(case
when total_profit > 0 then user_id end) as user_cnt
from t_case1_dwd_user_profit_status
group by stat_date
查询结果
+-------------+-----------+
| stat_date | user_cnt |
+-------------+-----------+
| 2026-03-12 | 1 |
| 2026-03-13 | 1 |
| 2026-03-14 | 3 |
| 2026-03-15 | 3 |
+-------------+-----------+
4 rows selected (0.725 seconds)本文参与 腾讯云自媒体同步曝光计划，分享自微信公众号。
原始发表：2026-05-19，如有侵权请联系 cloudcommunity@tencent.com 删除
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